Benchmarks

On data sets with about 1 million points in around 100 dimensions, FALCONN typically requires a few milliseconds per query (running on a reasonably modern desktop CPU).

For more detailed results, see ann-benchmarks of Erik Bernhardsson. Let us point out that FALCONN is especially competitive, when the RAM budget is quite restrictive, which is not the regime the above benchmarks use.

Publications

The underlying algorithms are described and analyzed in the following paper:

“Practical and Optimal LSH for Angular Distance” by A. Andoni, P. Indyk, T. Laarhoven, I. Razenshteyn and L. Schmidt, NIPS 2015, full version available at arXiv:1509.02897.